Closely-Spaced Object Classification Using MuyGPyS

• URL: http://arxiv.org/abs/2311.10904v1
• Date: Fri, 17 Nov 2023 22:52:46 GMT
• Title: Closely-Spaced Object Classification Using MuyGPyS
• Authors: Kerianne Pruett, Nathan McNaughton, and Michael Schneider
• Abstract summary: We present a novel method for detecting closely-spaced objects (CSO) in optical space domain awareness (SDA) algorithms. We use the Gaussian process python package, MuyGPyS, and examine classification accuracy as a function of angular separation and magnitude difference between simulated satellites. We find that MuyGPyS outperforms traditional machine learning methods, especially under more challenging circumstances.
• Score: 0.6144680854063939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately detecting rendezvous and proximity operations (RPO) is crucial for understanding how objects are behaving in the space domain. However, detecting closely-spaced objects (CSO) is challenging for ground-based optical space domain awareness (SDA) algorithms as two objects close together along the line-of-sight can appear blended as a single object within the point-spread function (PSF) of the optical system. Traditional machine learning methods can be useful for differentiating between singular objects and closely-spaced objects, but many methods require large training sample sizes or high signal-to-noise conditions. The quality and quantity of realistic data make probabilistic classification methods a superior approach, as they are better suited to handle these data inadequacies. We present CSO classification results using the Gaussian process python package, MuyGPyS, and examine classification accuracy as a function of angular separation and magnitude difference between the simulated satellites. This orbit-independent analysis is done on highly accurate simulated SDA images that emulate realistic ground-based commercial-of-the-shelf (COTS) optical sensor observations of CSOs. We find that MuyGPyS outperforms traditional machine learning methods, especially under more challenging circumstances.

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